EvoImp: Multiple Imputation of Multi-label Classification data with a genetic algorithm.

Missing data is a prevalent problem that requires attention, as most data analysis techniques are unable to handle it. This is particularly critical in Multi-Label Classification (MLC), where only a few studies have investigated missing data in this application domain. MLC differs from Single-Label Classification (SLC) by allowing an instance to be associated with multiple classes.

The Adventures of Amaru: Integrating Learning Tasks Into a Digital Game for Teaching Children in Early Phases of Literacy.

In low-income countries, the history of academic failure is a liability for children acquiring literacy skills. It is thus important to develop strategies that motivate and focus these students on specific strategies to learn to read. Digital games can be useful in motivating students and assisting teachers in the teaching-learning process, but there are few interactive tools that effectively integrate tasks of direct instruction and good gameplay.

Identification of miRNAs Expression Profile in Gastric Cancer Using Self-Organizing Maps (SOM).

In this paper, an unsupervised artificial neural network was implemented to identify the patters of specific signatures. The network was based on the differential expression of miRNAs (under or over expression) found in healthy or cancerous gastric tissues. Among the tissues analyzes, the neural network evaluated 514 miRNAs of gastric tissue that exhibited significant differential expression.